Digital subscriber line (DSL) is a modem technology that enables broadband high-speed data services over the telephone network in a cost-effective way. However, the telephone network was originally designed to deliver plain old telephone service (POTS), i.e., narrowband service. Therefore, it is important to evaluate whether a telephone line (loop) is capable of supporting a certain DSL service before its provisioning. This evaluation process is referred to as loop qualification (LQ).
The target of the loop qualification is to estimate the attainable bit rate supported by the loop under test (LUT). In particular, it is desired to achieve efficient qualification methods without dispatching technicians to the customer premise (CP) side. Hence, the development of a loop qualification method based on single-ended line testing (SELT) from the central office (CO) is crucial for consistent mass DSL deployment. The attainable bit rate can be determined if the transfer function as a function of the frequency and the noise profile of the loop are known. The noise profile may be estimated by, e.g., measurements at the CO side and the challenge is hence to estimate the transfer function in order to perform the loop qualification. FIG. 1 illustrates a telephone line being subject to loop qualification. At the central office (CO) side, the measured voltage is denoted V1 and the measured impedance is denoted Zin and at the CP side the measured voltage is denoted V2 and the measured impedance Zout.
Usually, the SELT based-methods used to estimate the transfer function rely on a priori knowledge about the LUT topology or on an identification process of it to estimate the loop transfer function posteriori. The transfer function of a subscriber loop may be estimated by means of a one-port scattering parameter measurement at the central office. One of such methods is presented in IEEE Journal on Selected Areas in Communications, 20(5):936-948, June 2002, where parameterized models for a transfer function and scattering parameter S11 of three predefined kinds of loop topologies are proposed. The optimum values for these parameters are found through maximum likelihood (ML) estimation, using the S11 model of the actual topology and S11 measurements to compose the cost-function of the optimization process. The estimated parameters are then applied to the transfer function model in order to estimate the loop transfer function. A method for topology identification using time domain reflectometry (TDR) is described in S. Galli and K. J. Kerpez, Single-ended loop make-up identification-part I: A method of analyzing TDR measurements, IEEE Transactions on Instrumentation and Measurement, 55(2):528-537, April 2006 and in K. J. Kerpez and S. Galli, Single-ended loop-makeup identification-part II: Improved algorithms and performance results, IEEE Transactions on Instrumentation and Measurement, 55(2):538-549, April 2006. The method is based on an iterative de-embedding process where the kinds of discontinuities on the LUT are first identified—using a database, a cable model and the mean squared error criterion—, followed by estimation of the sections length. The approach initially proposed in T. Vermeiren, T. Bostoen, P. Boets, X. Ochoa Chebab, and F. Louage, Subscriber loop topology classification by means of time domain reflectometry, Proceedings of the IEEE International Conference on Communications. ICC '03., volume 3, pages 1998-2002, May 2003 using S11 measurements in order to identify the loop topology is done by a set of well-defined phases: a) pre-processing of the measured S11 in order to generate its time domain counterpart S11(t) b) extraction of the features of each reflection present on S11(t) c) analyzing the extracted features through a Bayesian network and a rule based system in order to obtain knowledge about the loop topology as described in P. Boets, T. Bostoen, L. Van Biesen, and T. Pollet, Preprocessing of signals for single-ended subscriber line testing. IEEE Transactions on Instrumentation and Measurement, 55(5):1509-1518, October 2006, in Carine Neus, Patrick Boets, and Leo Van Biesen, Transfer function estimation of digital subscriber lines with single ended line testing, Proceedings of the IEEE Instrumentation and Measurement Technology Conference, pages 1-5, May 2007 and in Carine Neus, Patrick Boets, and Leo Van Biesen, Feature extraction of one port scattering parameters for single ended line testing. XVIII IMEKO World Congress, 2006.
The above described SELT based-methods depend on one or more tools like signal processing, Bayesian networks, algorithms to estimate the arrival time of the reflections, and optimization processes. In general, these tools are non-trivial ones and may have limited application in hardware implementation. Such methods also use cable models. Cable models are parametric models for the primary or secondary parameters of a cable section, in accordance to its gauge, and are useful to predict twisted-pair behavior. However, the nominal values (static) provided by a cable model and measurements may present an intrinsic mismatch since the real primary/secondary parameters may vary even from pair to pair of the same cable. This fact may delude methods based on comparison of simulated results and measurements. BT#1, MAR#2 and VUB0 are examples of cable models, and further described in R. F. M. van den Brink. Cable reference models for simulating metallic access networks, ETSI STC. TM6, 1998, J. Musson. Maximum likelihood estimation of the primary parameters of twisted pair cables. ETSI STC, TM6, 1998 and in P. Boets, M. Zekri, L. Van Biesen, T. Bostoen, and T. Pollet, On the identification of cables for metallic access networks, in Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference, IMTC '01, volume 2, pages 1348-1353, May 2001. Additionally, the estimation of the sections' length takes the mean value of the velocity of propagation (VoP) into account. A typical value is 65% of the speed of light in vacuum. However, strictly speaking, different cables have different velocity of propagation, temperature, age, humidity and other factors can affect the VoP.
As stated above, the transfer function estimation is essential for the LQ process. Based on the transfer function and noise profiles, the bit rate supported by the LUT can be calculated. However, the transfer function can only be measured by means of communication between equipments at both CO and CP side. This can be achieved by letting skilled service staff perform measurements at both the CO and CP sides. This is however costly. For some kinds of DSL applications it is possible to perform these measurements automatically using the transceivers at the CO together with the modem at the CP. However, this implies a working DSL connection which of course does not exist before the DSL deployment.
As the customer wants to know the attainable bit rate before he buys the service, SELT-based loop qualification methods aiming at estimating the loop transfer function are a great challenge for the providers of DSL services.